Advanced Brain Tumor Segmentation Using SAM2-UNet

Image segmentation is one of the key factors in diagnosing glioma patients with brain tumors. It helps doctors identify the types of tumor that a patient is carrying and will lead to a prognosis that will help save the lives of patients. The analysis of medical images is a specialized domain in comp...

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Bibliographic Details
Published in:Applied sciences Vol. 15; no. 6; p. 3267
Main Authors: Pidishetti, Rohit Viswakarma, Amjad, Maaz, Sheng, Victor S.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2025
Subjects:
Brain cancer
brain tumor segmentation
Brain tumors
BraTS 2020
Care and treatment
Datasets
Deep learning
Edema
Glioma
Gliomas
Localization
Machine vision
Magnetic resonance imaging
medical image segmentation
Medical imaging equipment
Medical research
Prognosis
segment anything model-2
Semantics
Tissues
Tumors
U-Network
Germany
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:Image segmentation is one of the key factors in diagnosing glioma patients with brain tumors. It helps doctors identify the types of tumor that a patient is carrying and will lead to a prognosis that will help save the lives of patients. The analysis of medical images is a specialized domain in computer vision and image processing. This process extracts meaningful information from medical images that helps in treatment planning and monitoring the condition of patients. Deep learning models like CNN have shown promising results in image segmentation by identifying complex patterns in the image data. These methods have also shown great results in tumor segmentation and the identification of anomalies, which assist health care professionals in treatment planning. Despite advancements made in the domain of deep learning for medical image segmentation, the precise segmentation of tumors remains challenging because of the complex structures of tumors across patients. Existing models, such as traditional U-Net- and SAM-based architectures, either lack efficiency in handling class-specific segmentation or require extensive computational resources. This study aims to bridge this gap by proposing Segment Anything Model 2-UNetwork, a hybrid model that leverages the strengths of both architectures to improve segmentation accuracy and consumes less computational resources by maintaining efficiency. The proposed model possesses the ability to perform explicitly well on scarce data, and we trained this model on the Brain Tumor Segmentation Challenge 2020 (BraTS) dataset. This architecture is inspired by U-Networks that are based on the encoder and decoder architecture. The Hiera pre-trained model is set as a backbone to this architecture to capture multi-scale features. Adapters are embedded into the encoder to achieve parameter-efficient fine-tuning. The dataset contains four channels of MRI scans of 369 glioma patients as T1, T1ce, T2, and T2-flair and a segmentation mask for each patient consisting of non-tumor (NT), necrotic and non-enhancing tumor (NCR/NET), and peritumoral edema or GD-enhancing tumor (ET) as the ground-truth value. These experiments yielded good segmentation performance and achieved balanced performance based on the metrics discussed next in this paragraph for each tumor region. Our experiments yielded the following results with minimal hardware resources, i.e., 16 GB RAM with 30 epochs: a mean Dice score (mDice) of 0.771, a mean Intersection over Union (mIoU) of 0.569, an Sα score of 0.692, a weighted F-beta score (Fβw) of 0.267, a F-beta score (Fβ) of 0.261, an Eϕ score of 0.857, and a Mean Absolute Error (MAE) of 0.04 on the BraTS 2020 dataset.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app15063267